Testing out websocket client and server



Websocket server 

To spin up a server that serve ws request, you can use the following code




Run "dep init"

Run "dep ensure -update"


Websocket client

npm install -g wscat

wscat -c ws://localhost:8002

You will see that you're connected. 


Comments

Post a Comment

Popular posts from this blog

gemini cli getting file not defined error

Image
 While trying to run gemini cli on my windows linux subsystem, i bump into this error here - "/Roaming/npm/node_modules/@google/gemini-cli/node_modules/undici/lib/web/webidl/index.js:512 webidl.is.File = webidl.util.MakeTypeAssertion(File)"  "ReferenceError: File is not defined" In Node.js, there’s no built-in File (until very recent Node 20+ with experimental WHATWG APIs). So, unless polyfilled, File is undefined, leading to the ReferenceError. And to resolve this, I had to upgrade to node 22 (might work for node 20) and then I was able to run it.
Read more

mongosh install properly

To install mongosh property on ubuntu 24.04, we need to wget -qO- https://www.mongodb.org/static/pgp/server-8.0.asc | sudo tee /etc/apt/trusted.gpg.d/server-8.0.asc Then run  echo "deb [ arch=amd64,arm64 ] https://repo.mongodb.org/apt/ubuntu noble/mongodb-org/8.0 multiverse" | sudo tee /etc/apt/sources.list.d/mongodb-org-8.0.list Next we will run  sudo apt-get update And then finally install it using  sudo apt-get install -y mongodb-mongosh To verify if it is running we run  mongosh --version
Read more

llama cpp running it in google colab

Image
There are 2 options to do this in Google  colab.  Option 1 (Easiest)  With this option, we can just install the relevant package and then run the model.  First we need to install the required packages using the following command:-  ! pip install -U llama-cpp-python Next,  we will try to get the model using the following command # !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained(   repo_id= "unsloth/Qwen3-0.6B-GGUF" ,   filename= "Qwen3-0.6B-IQ4_NL.gguf" , ) We can see the model being downloaded:- And then we will run the following command to test this model llm.create_chat_completion(   messages = [     {       "role" : "user" ,       "content" : "What is the capital of France?"     }   ] ) And the output will look something like this:-  Option 2 llama.cpp is a powerful inference engine and if you wanted to get it running in google colab, you co...
Read more